mGPT: fine-tune on message data - 2E
- This model is a fine-tuned version of sberbank-ai/mGPT on 80k messages. This builds on the minimum-working-example checkpoint here.
- 2E = 2 epochs
Model description
- testing if fine-tuned personality data bleeds over to other languages without being trained in them explicitly
Interesting findings thus far:
- Passing a generic word after the
<name-identifier>
that is in a non-English language helps ensure the model responds in the question language (see: any example). - Model generations (in general) remain semantically consistent, even if the generations switch from
<language>
to English in the middle of the generated text. This demonstrates some sort of "universal concept understanding"
Usage in python
Install the transformers library if you don't have it:
pip install -U transformers
load the model into a pipeline object:
from transformers import pipeline
import torch
device = 'cuda' if torch.cuda.is_available() else 'cpu'
my_chatbot = pipeline('text-generation',
'pszemraj/mGPT-Peter-2E',
device=0 if device == 'cuda' else -1,
)
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 1 (in addition to all training on prior checkpoints)
Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
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